from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFTransfoXLForSequenceClassification,
	    TFTransfoXLLMHeadModel,
	    TFTransfoXLModel,
	)

class        UpperCamelCase						:





						def __init__(self			,     __UpperCamelCase			,     )				->				List[Any]:
												UpperCamelCase_		:							Union[str, Any]  =		parent
												UpperCamelCase_		:							Union[str, Any]  =		13
												UpperCamelCase_		:							Union[str, Any]  =		7
												UpperCamelCase_		:							Optional[int]  =		30
												UpperCamelCase_		:							List[str]  =		self.seq_length + self.mem_len
												UpperCamelCase_		:							Optional[Any]  =		15
												UpperCamelCase_		:							List[str]  =		True
												UpperCamelCase_		:							Tuple  =		True
												UpperCamelCase_		:							Optional[int]  =		99
												UpperCamelCase_		:							Dict  =		[10, 50, 80]
												UpperCamelCase_		:							Dict  =		32
												UpperCamelCase_		:							int  =		32
												UpperCamelCase_		:							Dict  =		4
												UpperCamelCase_		:							str  =		8
												UpperCamelCase_		:							int  =		128
												UpperCamelCase_		:							List[Any]  =		2
												UpperCamelCase_		:							Optional[Any]  =		2
												UpperCamelCase_		:							str  =		None
												UpperCamelCase_		:							List[Any]  =		1
												UpperCamelCase_		:							Union[str, Any]  =		0
												UpperCamelCase_		:							Dict  =		3
												UpperCamelCase_		:							Any  =		self.vocab_size - 1
												UpperCamelCase_		:							Optional[int]  =		0.01





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							Any  =		ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size						)
												UpperCamelCase_		:							List[Any]  =		ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size						)

												UpperCamelCase_		:							Union[str, Any]  =		None
												if self.use_labels:
																		UpperCamelCase_		:							Optional[int]  =		ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size						)

												UpperCamelCase_		:							Any  =		TransfoXLConfig(
												    vocab_size=self.vocab_size			,     mem_len=self.mem_len			,     clamp_len=self.clamp_len			,     cutoffs=self.cutoffs			,     d_model=self.hidden_size			,     d_embed=self.d_embed			,     n_head=self.num_attention_heads			,     d_head=self.d_head			,     d_inner=self.d_inner			,     div_val=self.div_val			,     n_layer=self.num_hidden_layers			,     eos_token_id=self.eos_token_id			,     pad_token_id=self.vocab_size - 1			,     init_range=self.init_range			,     num_labels=self.num_labels			,     )

												return (config, input_ids_a, input_ids_a, lm_labels)





						def        A_							(self						)				->				int:
												random.seed(self.seed						)
												tf.random.set_seed(self.seed						)





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Optional[Any]:
												UpperCamelCase_		:							Any  =		TFTransfoXLModel(snake_case__						)

												UpperCamelCase_,UpperCamelCase_		:							List[Any]  =		model(snake_case__						).to_tuple()

												UpperCamelCase_		:							int  =		{"""input_ids""": input_ids_a, """mems""": mems_a}

												UpperCamelCase_,UpperCamelCase_		:							List[str]  =		model(snake_case__						).to_tuple()

												self.parent.assertEqual(hidden_states_a.shape			,     (self.batch_size, self.seq_length, self.hidden_size)						)
												self.parent.assertEqual(hidden_states_a.shape			,     (self.batch_size, self.seq_length, self.hidden_size)						)
												self.parent.assertListEqual(
												    [mem.shape for mem in mems_a]			,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,     )
												self.parent.assertListEqual(
												    [mem.shape for mem in mems_a]			,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,     )





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Tuple:
												UpperCamelCase_		:							Tuple  =		TFTransfoXLLMHeadModel(snake_case__						)

												UpperCamelCase_,UpperCamelCase_		:							int  =		model(snake_case__						).to_tuple()

												UpperCamelCase_		:							Tuple  =		{"""input_ids""": input_ids_a, """labels""": lm_labels}
												UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		model(snake_case__						).to_tuple()

												UpperCamelCase_,UpperCamelCase_		:							List[Any]  =		model([input_ids_a, mems_a]						).to_tuple()

												UpperCamelCase_		:							Union[str, Any]  =		{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

												UpperCamelCase_,UpperCamelCase_		:							str  =		model(snake_case__						).to_tuple()

												self.parent.assertEqual(lm_logits_a.shape			,     (self.batch_size, self.seq_length, self.vocab_size)						)
												self.parent.assertListEqual(
												    [mem.shape for mem in mems_a]			,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,     )

												self.parent.assertEqual(lm_logits_a.shape			,     (self.batch_size, self.seq_length, self.vocab_size)						)
												self.parent.assertListEqual(
												    [mem.shape for mem in mems_a]			,     [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers			,     )





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Optional[Any]:
												UpperCamelCase_		:							Dict  =		TFTransfoXLForSequenceClassification(snake_case__						)
												UpperCamelCase_		:							Optional[Any]  =		model(snake_case__						)
												self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)						)





						def        A_							(self						)				->				str:
												UpperCamelCase_		:							List[str]  =		self.prepare_config_and_inputs()
												((UpperCamelCase_),(UpperCamelCase_),(UpperCamelCase_),(UpperCamelCase_))		:							Optional[int]  =		config_and_inputs
												UpperCamelCase_		:							int  =		{"""input_ids""": input_ids_a}
												return config, inputs_dict


@require_tf
class        UpperCamelCase						(				SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	unittest.TestCase				):
						a__		:List[str]      =			(
						    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
						)
						a__		:Tuple      =			() if is_tf_available() else ()
						a__		:int      =			(
						    {
						        '''feature-extraction''': TFTransfoXLModel,
						        '''text-classification''': TFTransfoXLForSequenceClassification,
						        '''text-generation''': TFTransfoXLLMHeadModel,
						        '''zero-shot''': TFTransfoXLForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
						a__		:Any      =			False
						a__		:Union[str, Any]      =			False
						a__		:int      =			False
						a__		:Optional[int]      =			False





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)				->				List[Any]:
												if pipeline_test_casse_name == "TextGenerationPipelineTests":
																		# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
																		# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
																		# tokenizer.
																		return True

												return False





						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							Optional[Any]  =		TFTransfoXLModelTester(self						)
												UpperCamelCase_		:							Optional[Any]  =		ConfigTester(self			,     config_class=snake_case__			,     d_embed=37						)





						def        A_							(self						)				->				Tuple:
												self.config_tester.run_common_tests()





						def        A_							(self						)				->				Any:
												self.model_tester.set_seed()
												UpperCamelCase_		:							Union[str, Any]  =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_model(*snake_case__						)





						def        A_							(self						)				->				Tuple:
												self.model_tester.set_seed()
												UpperCamelCase_		:							int  =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_lm_head(*snake_case__						)





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_		:							List[Any]  =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*snake_case__						)





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_,UpperCamelCase_		:							str  =		self.model_tester.prepare_config_and_inputs_for_common()
												UpperCamelCase_		:							Union[str, Any]  =		[TFTransfoXLForSequenceClassification]

												for model_class in self.all_model_classes:
																		UpperCamelCase_		:							Optional[Any]  =		model_class(snake_case__						)
																		assert isinstance(model.get_input_embeddings()			,     tf.keras.layers.Layer						)
																		if model_class in list_other_models_with_output_ebd:
																								UpperCamelCase_		:							Any  =		model.get_output_embeddings()
																								assert isinstance(snake_case__			,     tf.keras.layers.Layer						)
																								UpperCamelCase_		:							Optional[int]  =		model.get_bias()
																								assert name is None
																		else:
																								UpperCamelCase_		:							Optional[Any]  =		model.get_output_embeddings()
																								assert x is None
																								UpperCamelCase_		:							Dict  =		model.get_bias()
																								assert name is None





						def        A_							(self						)				->				Dict:
												# TODO JP: Make TransfoXL XLA compliant
												pass





						@slow
						def        A_							(self						)				->				Union[str, Any]:
												for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCamelCase_		:							Any  =		TFTransfoXLModel.from_pretrained(snake_case__						)
																		self.assertIsNotNone(snake_case__						)





						@unittest.skip(reason="""This model doesn\'t play well with fit() due to not returning a single loss."""						)
						def        A_							(self						)				->				List[Any]:
												pass


@require_tf
class        UpperCamelCase						(				unittest.TestCase				):





						@unittest.skip("""Skip test until #12651 is resolved."""						)
						@slow
						def        A_							(self						)				->				Optional[Any]:
												UpperCamelCase_		:							str  =		TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""						)
												# fmt: off
												UpperCamelCase_		:							int  =		tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]			,     dtype=tf.intaa						)  # noqa: E231
												# fmt: on
												#  In 1991 , the remains of Russian Tsar Nicholas II and his family
												#  ( except for Alexei and Maria ) are discovered .
												#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
												#  remainder of the story . 1883 Western Siberia ,
												#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
												#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
												#  father initially slaps him for making such an accusation , Rasputin watches as the
												#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
												#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
												#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

												# fmt: off
												UpperCamelCase_		:							List[str]  =		[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
												# fmt: on
												#  In 1991, the remains of Russian Tsar Nicholas II and his family (
												#  except for Alexei and Maria ) are discovered. The voice of young son,
												#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
												#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
												#  and a group of men to perform magic. Rasputin has a vision and
												#  denounces one of the men as a horse thief. Although his father initially
												#  slaps him for making such an accusation, Rasputin watches as the man
												#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
												#  of the Virgin Mary, prompting him to become a priest.
												#  Rasputin quickly becomes famous, with people, even a bishop, begging for
												#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
												# Nicholas II and his family were discovered. The voice of <unk> young son,
												# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

												UpperCamelCase_		:							Dict  =		model.generate(snake_case__			,     max_length=200			,     do_sample=snake_case__						)
												self.assertListEqual(output_ids[0].numpy().tolist()			,     snake_case__						)







import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def 	lowerCamelCase					(	UpperCamelCase     :				int							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  torch.exp(UpperCamelCase							)
		_lowerCamelCase				   =  torch.sum(UpperCamelCase			,       dim=1							)  # sum of exp(x_i)
		_lowerCamelCase				   =  torch.sum(x * exp_x			,       dim=1							)  # sum of x_i * exp(x_i)
		return torch.log(UpperCamelCase							) - B / A





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     Any		)      ->	Optional[Any]:
				super().__init__()
				_lowerCamelCase				   =  config.output_attentions
				_lowerCamelCase				   =  config.output_hidden_states
				_lowerCamelCase				   =  nn.ModuleList([BertLayer(snake_case__		) for _ in range(config.num_hidden_layers		)]		)
				_lowerCamelCase				   =  nn.ModuleList([BertHighway(snake_case__		) for _ in range(config.num_hidden_layers		)]		)

				_lowerCamelCase				   =  [-1 for _ in range(config.num_hidden_layers		)]
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     str		)      ->	Optional[int]:
				if (type(snake_case__		) is float) or (type(snake_case__		) is int):
						for i in range(len(self.early_exit_entropy		)		):
								_lowerCamelCase				   =  x
				else:
						_lowerCamelCase				   =  x
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     Optional[int]		)      ->	int:
				_lowerCamelCase				   =  pooler.state_dict()
				for highway in self.highway:
						for name, param in highway.pooler.state_dict().items():
								param.copy_(loaded_model[name]		)



		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     int=None     ,  snake_case__     :     Optional[int]=None     ,  snake_case__     :     List[str]=None     ,  snake_case__     :     Dict=None     ,  )      ->	List[str]:
				_lowerCamelCase				   =  ()
				_lowerCamelCase				   =  ()
				_lowerCamelCase				   =  ()
				for i, layer_module in enumerate(self.layer		):
						if self.output_hidden_states:
								_lowerCamelCase				   =  all_hidden_states + (hidden_states,)

						_lowerCamelCase				   =  layer_module(
						    snake_case__     ,  snake_case__     ,  head_mask[i]     ,  snake_case__     ,  snake_case__		)
						_lowerCamelCase				   =  layer_outputs[0]

						if self.output_attentions:
								_lowerCamelCase				   =  all_attentions + (layer_outputs[1],)

						_lowerCamelCase				   =  (hidden_states,)
						if self.output_hidden_states:
								_lowerCamelCase				   =  current_outputs + (all_hidden_states,)
						if self.output_attentions:
								_lowerCamelCase				   =  current_outputs + (all_attentions,)

						_lowerCamelCase				   =  self.highway[i](snake_case__		)
						# logits, pooled_output

						if not self.training:
								_lowerCamelCase				   =  highway_exit[0]
								_lowerCamelCase				   =  entropy(snake_case__		)
								_lowerCamelCase				   =  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
								_lowerCamelCase				   =  all_highway_exits + (highway_exit,)

								if highway_entropy < self.early_exit_entropy[i]:
										_lowerCamelCase				   =  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
										raise HighwayException(snake_case__     ,  i + 1		)
						else:
								_lowerCamelCase				   =  all_highway_exits + (highway_exit,)

        # Add last layer
				if self.output_hidden_states:
						_lowerCamelCase				   =  all_hidden_states + (hidden_states,)

				_lowerCamelCase				   =  (hidden_states,)
				if self.output_hidden_states:
						_lowerCamelCase				   =  outputs + (all_hidden_states,)
				if self.output_attentions:
						_lowerCamelCase				   =  outputs + (all_attentions,)

				_lowerCamelCase				   =  outputs + (all_highway_exits,)
				return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits





@add_start_docstrings(
    'The Bert Model transformer with early exiting (DeeBERT). '     ,SCREAMING_SNAKE_CASE_     ,)
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     List[Any]		)      ->	Any:
				super().__init__(snake_case__		)
				_lowerCamelCase				   =  config

				_lowerCamelCase				   =  BertEmbeddings(snake_case__		)
				_lowerCamelCase				   =  DeeBertEncoder(snake_case__		)
				_lowerCamelCase				   =  BertPooler(snake_case__		)

				self.init_weights()
		def   _snake_case       (       self     :     Tuple		)      ->	Tuple:
				self.encoder.init_highway_pooler(self.pooler		)
		def   _snake_case       (       self     :     int		)      ->	List[Any]:
				return self.embeddings.word_embeddings
		def   _snake_case       (       self     :     Optional[int]     ,  snake_case__     :     List[Any]		)      ->	Any:
				_lowerCamelCase				   =  value
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				for layer, heads in heads_to_prune.items():
						self.encoder.layer[layer].attention.prune_heads(snake_case__		)



		@add_start_docstrings_to_model_forward(snake_case__		)
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Dict=None     ,  snake_case__     :     Any=None     ,  snake_case__     :     Dict=None     ,  snake_case__     :     Any=None     ,  snake_case__     :     List[str]=None     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     str=None     ,  snake_case__     :     List[Any]=None     ,  )      ->	List[str]:
				if input_ids is not None and inputs_embeds is not None:
						raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'		)
				elif input_ids is not None:
						_lowerCamelCase				   =  input_ids.size()
				elif inputs_embeds is not None:
						_lowerCamelCase				   =  inputs_embeds.size()[:-1]
				else:
						raise ValueError('You have to specify either input_ids or inputs_embeds'		)

				_lowerCamelCase				   =  input_ids.device if input_ids is not None else inputs_embeds.device

				if attention_mask is None:
						_lowerCamelCase				   =  torch.ones(snake_case__     ,  device=snake_case__		)
				if encoder_attention_mask is None:
						_lowerCamelCase				   =  torch.ones(snake_case__     ,  device=snake_case__		)
				if token_type_ids is None:
						_lowerCamelCase				   =  torch.zeros(snake_case__     ,  dtype=torch.long     ,  device=snake_case__		)

				# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
				# ourselves in which case we just need to make it broadcastable to all heads.
				_lowerCamelCase				   =  self.get_extended_attention_mask(snake_case__     ,  snake_case__     ,  snake_case__		)

				# If a 2D ou 3D attention mask is provided for the cross-attention
				# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
				if encoder_attention_mask.dim() == 3:
						_lowerCamelCase				   =  encoder_attention_mask[:, None, :, :]
				if encoder_attention_mask.dim() == 2:
						_lowerCamelCase				   =  encoder_attention_mask[:, None, None, :]

				_lowerCamelCase				   =  encoder_extended_attention_mask.to(
				    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
				_lowerCamelCase				   =  (1.0 - encoder_extended_attention_mask) * -1_0000.0

				# Prepare head mask if needed
				# 1.0 in head_mask indicate we keep the head
				# attention_probs has shape bsz x n_heads x N x N
				# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
				# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
				_lowerCamelCase				   =  self.get_head_mask(snake_case__     ,  self.config.num_hidden_layers		)

				_lowerCamelCase				   =  self.embeddings(
				    input_ids=snake_case__     ,  position_ids=snake_case__     ,  token_type_ids=snake_case__     ,  inputs_embeds=snake_case__		)
				_lowerCamelCase				   =  self.encoder(
				    snake_case__     ,  attention_mask=snake_case__     ,  head_mask=snake_case__     ,  encoder_hidden_states=snake_case__     ,  encoder_attention_mask=snake_case__     ,  )
				_lowerCamelCase				   =  encoder_outputs[0]
				_lowerCamelCase				   =  self.pooler(snake_case__		)

				_lowerCamelCase				   =  (
				    sequence_output,
				    pooled_output,
				) + encoder_outputs[
				    1:
				]  # add hidden_states and attentions if they are here
				return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Optional[int]     ,  snake_case__     :     Dict     ,  snake_case__     :     Any		)      ->	str:
				_lowerCamelCase				   =  message
				_lowerCamelCase				   =  exit_layer  # start from 1!





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     Optional[Any]		)      ->	Any:
				super().__init__()
				_lowerCamelCase				   =  BertPooler(snake_case__		)
				_lowerCamelCase				   =  nn.Dropout(config.hidden_dropout_prob		)
				_lowerCamelCase				   =  nn.Linear(config.hidden_size     ,  config.num_labels		)



		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     Dict		)      ->	Optional[Any]:
				# Pooler
				_lowerCamelCase				   =  encoder_outputs[0]
				_lowerCamelCase				   =  self.pooler(snake_case__		)
				# "return" pooler_output

				# BertModel
				_lowerCamelCase				   =  (pooler_input, pooler_output) + encoder_outputs[1:]
				# "return" bmodel_output

				# Dropout and classification
				_lowerCamelCase				   =  bmodel_output[1]

				_lowerCamelCase				   =  self.dropout(snake_case__		)
				_lowerCamelCase				   =  self.classifier(snake_case__		)

				return logits, pooled_output





@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '     ,SCREAMING_SNAKE_CASE_     ,)
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Optional[int]     ,  snake_case__     :     Dict		)      ->	Dict:
				super().__init__(snake_case__		)
				_lowerCamelCase				   =  config.num_labels
				_lowerCamelCase				   =  config.num_hidden_layers

				_lowerCamelCase				   =  DeeBertModel(snake_case__		)
				_lowerCamelCase				   =  nn.Dropout(config.hidden_dropout_prob		)
				_lowerCamelCase				   =  nn.Linear(config.hidden_size     ,  self.config.num_labels		)

				self.init_weights()



		@add_start_docstrings_to_model_forward(snake_case__		)
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     Optional[int]=None     ,  snake_case__     :     str=None     ,  snake_case__     :     Dict=None     ,  snake_case__     :     str=None     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     int=None     ,  snake_case__     :     Union[str, Any]=-1     ,  snake_case__     :     Any=False     ,  )      ->	Union[str, Any]:
				_lowerCamelCase				   =  self.num_layers
				try:
						_lowerCamelCase				   =  self.bert(
						    snake_case__     ,  attention_mask=snake_case__     ,  token_type_ids=snake_case__     ,  position_ids=snake_case__     ,  head_mask=snake_case__     ,  inputs_embeds=snake_case__     ,  )
						# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

						_lowerCamelCase				   =  outputs[1]

						_lowerCamelCase				   =  self.dropout(snake_case__		)
						_lowerCamelCase				   =  self.classifier(snake_case__		)
						_lowerCamelCase				   =  (logits,) + outputs[2:]  # add hidden states and attention if they are here
				except HighwayException as e:
						_lowerCamelCase				   =  e.message
						_lowerCamelCase				   =  e.exit_layer
						_lowerCamelCase				   =  outputs[0]

				if not self.training:
						_lowerCamelCase				   =  entropy(snake_case__		)
						_lowerCamelCase				   =  []
						_lowerCamelCase				   =  []
				if labels is not None:
						if self.num_labels == 1:
								#  We are doing regression
								_lowerCamelCase				   =  MSELoss()
								_lowerCamelCase				   =  loss_fct(logits.view(-1		)     ,  labels.view(-1		)		)
						else:
								_lowerCamelCase				   =  CrossEntropyLoss()
								_lowerCamelCase				   =  loss_fct(logits.view(-1     ,  self.num_labels		)     ,  labels.view(-1		)		)

						# work with highway exits
						_lowerCamelCase				   =  []
						for highway_exit in outputs[-1]:
								_lowerCamelCase				   =  highway_exit[0]
								if not self.training:
										highway_logits_all.append(snake_case__		)
										highway_entropy.append(highway_exit[2]		)
								if self.num_labels == 1:
										#  We are doing regression
										_lowerCamelCase				   =  MSELoss()
										_lowerCamelCase				   =  loss_fct(highway_logits.view(-1		)     ,  labels.view(-1		)		)
								else:
										_lowerCamelCase				   =  CrossEntropyLoss()
										_lowerCamelCase				   =  loss_fct(highway_logits.view(-1     ,  self.num_labels		)     ,  labels.view(-1		)		)
								highway_losses.append(snake_case__		)

						if train_highway:
								_lowerCamelCase				   =  (sum(highway_losses[:-1]		),) + outputs
								# exclude the final highway, of course
						else:
								_lowerCamelCase				   =  (loss,) + outputs
				if not self.training:
						_lowerCamelCase				   =  outputs + ((original_entropy, highway_entropy), exit_layer)
						if output_layer >= 0:
								_lowerCamelCase				   =  (
								    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
								)  # use the highway of the last layer

				return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)